Evidence map›Paper›PMID 41855744›Full record

ArticleJACC. Advances2026

Deep Learning Echocardiographic Trajectories of Heart Failure With Preserved Ejection Fraction: A Retrospective Cohort Study.

Erick Romero, Hitalo Oliveira da Silva, Padmini Sirish, Javier E Lopez, Nipavan Chiamvimonvat, David A Liem, Martin Cadeiras

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Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Erick RomeroDepartment of Internal Medicine, SBH Health System, Bronx, New York, USA. Electronic address: eromero@sbhny.org.
Hitalo Oliveira da SilvaDivision of Cardiovascular Medicine, UC Davis Medical Center, Sacramento, California, USA.
Padmini SirishDivision of Cardiovascular Medicine, University of California, Davis, California, USA.
Javier E LopezDepartment of Internal Medicine, SBH Health System, Bronx, New York, USA.
Nipavan ChiamvimonvatDivision of Cardiovascular Medicine, University of California, Davis, California, USA.
David A LiemDivision of Cardiovascular Medicine, UC Davis Medical Center, Sacramento, California, USA.
Martin CadeirasDivision of Cardiovascular Medicine, UC Davis Medical Center, Sacramento, California, USA. Electronic address: mcadeiras@ucdavis.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure with preserved ejection fraction (HFpEF) is a dynamic chronic disease. Specific disease trajectories remain unclear.

objectivesThis study aims to investigate cardiac structure and function trajectories measured by echocardiography and their relationship with clinical outcomes.

methodsThis was a retrospective cohort from a single center of HFpEF patients with follow-up echocardiogram at ≥ 1 year. Seven longitudinal echocardiographic variables of cardiac geometry and function were utilized to identify trajectory. Longitudinal phenomapping analysis was conducted with machine and deep learning longitudinal clustering. The primary outcome was all-cause mortality, while the secondary outcome consisted of changes in B-type natriuretic peptide levels overtime. A panel of longitudinal laboratory biomarkers was used for exploratory outcomes.

resultsIn total, 1,626 HFpEF cases were included; training and validation sets were randomly derived. Echocardiographic trajectories were identified: Echo-Trajectory #1, a stable remodeling trajectory; Echo-Trajectory #2, an increased remodeling course; and Echo-Trajectory #3, decreased remodeling trajectory with right ventricular dysfunction. Baseline clinical characteristics varied significantly among Echo-Trajectories by sex, age, blood pressure, obesity, comorbidities, and left ventricular mass index (P < 0.05). Compared to Echo-Trajectories #2 and #3, Trajectory #1 had a better all-cause mortality outcome (P < 0.001). Similarly, Echo-Trajectory #1 presented favorable trajectories of natriuretic peptides, sodium, and renal function (P < 0.05).

conclusionsLongitudinal phenomapping resulted in echocardiographic trajectories of cardiac structure and function with different clinical, laboratory, and survival characteristics. HFpEF is a dynamic condition, and longitudinal phenomapping may improve trajectory characterization and guide treatment strategies.

Indexed as

deep learningechocardiogramHFpEFphenomappingtrajectories

Identifiers

PMID41855744
PMCPMC13010401

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.